The present invention relates to an instance usage facilitating system.
In cloud computing which utilizes computer resources existing across a network and causes them to execute a large variety of computation, a large number of computer resources are sometimes allocated to some users. Some of these computer resources appear not to be used actually, even though only allocated.
In this situation, when a lot of users want to use computer resources, because such a large number of resources have already been allocated, though available resources exist, a shortage of resources that can be used may occur consequently and computation may not be performed efficiently.
In order to avoid a shortage of instances as computer resources in such a situation, a technical approach outlined below is proposed: after machine learning of, e.g., a mapping relation between metrics acquired from respective instances existing across a network and actual operating status of the instances, as teacher data, is carried out, by estimating the operating status of each instance using a machine learning method by which the foregoing learning has been done, the technical approach facilitates reuse of idle instances (refer to, e.g., Kee Kim, et al., 2006, A Supervised Learning Model for Identifying Inactive VMs in Private Cloud Data Centers. In Proceedings of the Industrial Track of the 17th International Middleware Conference (Middleware Industry '16). ACM.).
According to this technical approach, after sufficient machine learning is carried out, it is possible to find an idle instance from among plural instances with a high degree of accuracy and make the idle instance usable again and it is expected to avoid a shortage of instances in totality.